From 54246459bda7656a987b4b4ea77ae8a998f354db Mon Sep 17 00:00:00 2001
From: Ted Sanders <ted@openai.com>
Date: Thu, 20 Apr 2023 21:38:35 -0700
Subject: [PATCH] removes old references to query and doc models

---
 examples/Semantic_text_search_using_embeddings.ipynb | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/Semantic_text_search_using_embeddings.ipynb b/examples/Semantic_text_search_using_embeddings.ipynb
index 6451ee6..6155dc9 100644
--- a/examples/Semantic_text_search_using_embeddings.ipynb
+++ b/examples/Semantic_text_search_using_embeddings.ipynb
@@ -1,12 +1,13 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Semantic text search using embeddings\n",
     "\n",
-    "We can search through all our reviews semantically in a very efficient manner and at very low cost, by simply embedding our search query, and then finding the most similar reviews. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
+    "We can search through all our reviews semantically in a very efficient manner and at very low cost, by embedding our search query, and then finding the most similar reviews. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
    ]
   },
   {
@@ -25,10 +26,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Remember to use the documents embedding engine for documents (in this case reviews), and query embedding engine for queries. Note that here we just compare the cosine similarity of the embeddings of the query and the documents, and show top_n best matches."
+    "Here we compare the cosine similarity of the embeddings of the query and the documents, and show top_n best matches."
    ]
   },
   {